• Traditional (base) graphics
  • isolated instructions to the device

• Traditional (base) graphics
  • isolated instructions to the device
• Grid graphics
  • instruction sets
  • lattice
  • ggplot2

library(ggplot2) #OR better still library(tidyverse)
library(grid)
library(gridExtra)
library(patchwork)
library(scales)

• layers of data driven objects
• coordinate system
• scales
• faceting
• themes

head(BOD)

##   Time demand
## 1    1    8.3
## 2    2   10.3
## 3    3   19.0
## 4    4   16.0
## 5    5   15.6
## 6    7   19.8

summary(BOD)

##       Time           demand     
##  Min.   :1.000   Min.   : 8.30  
##  1st Qu.:2.250   1st Qu.:11.62  
##  Median :3.500   Median :15.80  
##  Mean   :3.667   Mean   :14.83  
##  3rd Qu.:4.750   3rd Qu.:18.25  
##  Max.   :7.000   Max.   :19.80

ggplot(data = BOD, map = aes(y = demand, x = Time)) +
    geom_point() +
    geom_line()

• data

p<-ggplot(data = BOD)

• layers (geoms)

p<-p + geom_point(aes(y = demand, x = Time))
p

• data

p<-ggplot(data = BOD)

• layers (geoms)

p<-p + geom_point(aes(y = demand, x = Time))

• scales

p <- p + scale_x_sqrt(name = "Time (days)")
p

• layers of data driven objects
  • geometric objects to represent data
  • statistical methods to summarize the data
  • mapping of aesthetics
  • position control

• coupled together
• engage either
• stat_identity

Arguments

• data - obvious
• mapping - aesthetics

If omitted, inherited from ggplot()

• stat - the stat_ function
• position - overlapping geoms

ggplot(data = BOD, aes(y = demand, x = Time)) + geom_point()
#OR
ggplot(data = BOD) + geom_point(aes(y = demand, x = Time))
                                        #OR
ggplot() + geom_point(data = BOD, aes(y = demand, x = Time))

• alpha - transparency
• colour - colour of the geometric features
• fill - colour of the geometric features
• linetype - fill colour of geometric features
• size - size of geometric features such as points or text
• shape - shape of geometric features such as points
• weight - weightings of values

head(CO2)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8
## 4   Qn1 Quebec nonchilled  350   37.2
## 5   Qn1 Quebec nonchilled  500   35.3
## 6   Qn1 Quebec nonchilled  675   39.2

summary(CO2)

##      Plant             Type         Treatment       conc          uptake     
##  Qn1    : 7   Quebec     :42   nonchilled:42   Min.   :  95   Min.   : 7.70  
##  Qn2    : 7   Mississippi:42   chilled   :42   1st Qu.: 175   1st Qu.:17.90  
##  Qn3    : 7                                    Median : 350   Median :28.30  
##  Qc1    : 7                                    Mean   : 435   Mean   :27.21  
##  Qc3    : 7                                    3rd Qu.: 675   3rd Qu.:37.12  
##  Qc2    : 7                                    Max.   :1000   Max.   :45.50  
##  (Other):42

ggplot(CO2) + geom_point(aes(x = conc, y = uptake), colour = "red")

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))

ggplot(CO2) + geom_point(aes(x = conc, y = uptake),
 stat = "summary", fun.y = mean)

head(diamonds)

## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

summary(diamonds)

##      carat               cut        color        clarity          depth      
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00  
##                                     J: 2808   (Other): 2531                  
##      table           price             x                y         
##  Min.   :43.00   Min.   :  326   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720  
##  Median :57.00   Median : 2401   Median : 5.700   Median : 5.710  
##  Mean   :57.46   Mean   : 3933   Mean   : 5.731   Mean   : 5.735  
##  3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540  
##  Max.   :95.00   Max.   :18823   Max.   :10.740   Max.   :58.900  
##                                                                   
##        z         
##  Min.   : 0.000  
##  1st Qu.: 2.910  
##  Median : 3.530  
##  Mean   : 3.539  
##  3rd Qu.: 4.040  
##  Max.   :31.800  
## 

Continuous variable

ggplot(diamonds) + geom_bar(aes(x = carat))

Categorical variable

ggplot(diamonds) + geom_bar(aes(x = cut))

Multiple categorical variables

ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity))

Multiple categorical variables

ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity),
  position='dodge')

ggplot(diamonds) + geom_boxplot(aes(y = carat))

ggplot(diamonds) + geom_boxplot(aes(x=carat))

ggplot(diamonds) + geom_boxplot(aes(x = cut, y = carat))

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_line(aes(x = conc, y = uptake))

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_line(aes(x = conc, y = uptake, group = Plant))

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_line(aes(x = conc, y = uptake, color=Plant))

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_line(aes(x = conc, y = uptake),
    stat = "summary", fun.y = mean, color='blue')

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_point(aes(x = conc, y = uptake))

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, fill = Treatment),
  shape=21)

head(CO2, 3)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake), method = "lm")

head(CO2, 3)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake, fill = Treatment))

library(maps)
library(mapdata)
aus <- map_data("worldHires", region="Australia")
head(aus,3)

##       long       lat group order    region              subregion
## 1 142.1461 -10.74943     1     1 Australia Prince of Wales Island
## 2 142.1430 -10.74525     1     2 Australia Prince of Wales Island
## 3 142.1406 -10.74113     1     3 Australia Prince of Wales Island

ggplot(aus, aes(x=long, y=lat, group=group)) +
    geom_polygon()

head(faithfuld,3) 

## # A tibble: 3 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384
## 3      1.69      43 0.00444

ggplot(faithfuld, aes(waiting, eruptions)) +
      geom_tile(aes(fill = density))

head(faithfuld,3)

## # A tibble: 3 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384
## 3      1.69      43 0.00444

ggplot(faithfuld, aes(waiting, eruptions)) +
      geom_raster(aes(fill = density))

head(warpbreaks)

##   breaks wool tension
## 1     26    A       L
## 2     30    A       L
## 3     54    A       L
## 4     25    A       L
## 5     70    A       L
## 6     52    A       L

summary(warpbreaks)

##      breaks      wool   tension
##  Min.   :10.00   A:27   L:18   
##  1st Qu.:18.25   B:27   M:18   
##  Median :26.00          H:18   
##  Mean   :28.15                 
##  3rd Qu.:34.00                 
##  Max.   :70.00

library(dplyr)
library(gmodels)
warpbreaks.sum <- warpbreaks %>%
    group_by(wool) %>%
    summarise(Mean = mean(breaks),
              Lower=ci(breaks)[2],
              Upper=ci(breaks)[3])
warpbreaks.sum

## # A tibble: 2 x 4
##   wool   Mean Lower Upper
## * <fct> <dbl> <dbl> <dbl>
## 1 A      31.0  24.8  37.3
## 2 B      25.3  21.6  28.9

ggplot(warpbreaks.sum) +
    geom_errorbar(aes(x = wool, ymin = Lower, ymax = Upper), width=0.2)

head(warpbreaks,3)

##   breaks wool tension
## 1     26    A       L
## 2     30    A       L
## 3     54    A       L

ggplot(warpbreaks) + geom_errorbar(aes(x = wool, y = breaks),
    stat = "summary", fun.data = "mean_cl_boot")

ggplot(warpbreaks.sum) +
    geom_linerange(aes(x = wool, ymin = Lower, ymax = Upper))

ggplot(warpbreaks.sum) +
    geom_pointrange(aes(y=Mean, x = wool, ymin = Lower, ymax = Upper))

head(CO2,3)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_point(aes(x = conc, y = uptake))+
  coord_cartesian() #default

head(CO2,3)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_point(aes(x = conc, y = uptake))+
  coord_polar()

head(CO2,3)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

ggplot(CO2) + geom_point(aes(x = conc, y = uptake))+
 coord_flip()

#Orthographic coordinates
library(maps)
library(mapdata)
aus <- map_data("worldHires", region = "Australia")
ggplot(aus, aes(x = long, y = lat, group = group)) +
  coord_map("ortho", orientation=c(-20, 125, 23.5))+
  geom_polygon()

Axis titles

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

ggplot(CO2, aes(y = uptake, x = conc)) + geom_point()+
  scale_x_continuous(name = "CO2 conc")

Axis titles with math

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

ggplot(CO2, aes(y = uptake, x = conc)) + geom_point()+
    scale_x_continuous(name = expression(
         Ambient~CO[2]~concentration~(mg/l)))

Axis more padding

ggplot(CO2, aes(y = uptake, x = conc)) + geom_point()+
  scale_x_continuous(name = "CO2 conc", expand = c(0,200))

Axis on a log scale

ggplot(CO2, aes(y = uptake, x = conc)) + geom_point()+
  scale_x_log10(name = "CO2 conc",
    breaks = as.vector(c(1, 2, 5, 10) %o% 10^(-1:2)))

Axis representing categorical data

ggplot(CO2, aes(y = uptake, x = Treatment)) + geom_point()+
  scale_x_discrete(name = "Treatment")

• size of points (thickness of lines)
• shape of points
• linetype of lines
• color of lines or points
• fill of shapes

Size according to continuous variable

state=data.frame(state.x77, state.region, state.division, state.center) %>%
    select(Illiteracy, state.region, x, y)
head(state,2)

##         Illiteracy state.region         x       y
## Alabama        2.1        South  -86.7509 32.5901
## Alaska         1.5         West -127.2500 49.2500

ggplot(state, aes(y = y, x = x)) + geom_point(aes(size = Illiteracy))

Discrete sizes ranging in size from 2 to 4

head(state,2)

##         Illiteracy state.region         x       y
## Alabama        2.1        South  -86.7509 32.5901
## Alaska         1.5         West -127.2500 49.2500

ggplot(state, aes(y = y,x = x)) + geom_point(aes(size = state.region))+
  scale_size_discrete(name = "Region", range = c(2, 10))

Manual sizes (2 and 4)

head(state,2)

##         Illiteracy state.region         x       y
## Alabama        2.1        South  -86.7509 32.5901
## Alaska         1.5         West -127.2500 49.2500

ggplot(state, aes(y = y, x = x)) + geom_point(aes(size = state.region))+
    scale_size_manual(name = "Region", values = c(2, 5, 6, 10))

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

ggplot(CO2, aes(y = uptake,x=conc)) + geom_point(aes(shape = Treatment))

CO2 = CO2 %>% mutate(Comb = interaction(Type, Treatment))
CO2 %>% pull(Comb) %>% levels

## [1] "Quebec.nonchilled"      "Mississippi.nonchilled" "Quebec.chilled"        
## [4] "Mississippi.chilled"

ggplot(CO2, aes(y = uptake,x = conc)) + geom_point(aes(shape = Comb))+
  scale_shape_discrete(name = "Type",
   labels=c("Quebec non-chilled","Quebec chilled",
            "Miss. non-chilled","Miss. chilled"))

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake              Comb
## 1   Qn1 Quebec nonchilled   95   16.0 Quebec.nonchilled
## 2   Qn1 Quebec nonchilled  175   30.4 Quebec.nonchilled

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth(aes(linetype = Comb)) +
  scale_linetype_discrete(name = "Type")

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake              Comb
## 1   Qn1 Quebec nonchilled   95   16.0 Quebec.nonchilled
## 2   Qn1 Quebec nonchilled  175   30.4 Quebec.nonchilled

ggplot(CO2, aes(y = uptake, x = conc)) +
  geom_smooth(aes(linetype = Treatment)) +
  scale_linetype_manual(name = "Treatment", values = c("dashed","dotted"))

head(faithfuld,2)

## # A tibble: 2 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384

ggplot(faithfuld, aes(waiting, eruptions)) +
    geom_raster(aes(fill = density)) +
    scale_fill_continuous(low = 'red', high = 'blue')

head(faithfuld,2)

## # A tibble: 2 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384

ggplot(faithfuld, aes(waiting, eruptions)) +
  geom_raster(aes(fill = density)) +
  scale_fill_gradient2(low = 'red', mid = 'white', high = 'blue',
                       midpoint = 0.02)

head(faithfuld,2)

## # A tibble: 2 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384

ggplot(faithfuld, aes(waiting, eruptions)) +
  geom_raster(aes(fill = density)) +
  scale_fill_gradientn(colours = terrain.colors(10))

head(faithfuld,2)

## # A tibble: 2 x 3
##   eruptions waiting density
##       <dbl>   <dbl>   <dbl>
## 1      1.6       43 0.00322
## 2      1.65      43 0.00384

ggplot(faithfuld, aes(waiting, eruptions)) +
  geom_raster(aes(fill = density)) +
  scale_fill_viridis_c(option = 'D')

##also try scale_fill_viridis_b
## also options= 'A', 'B', 'C'

RColorBrewer::display.brewer.all()

RColorBrewer::brewer.pal(n=8, name='Set1')

## [1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00" "#FFFF33" "#A65628"
## [8] "#F781BF"

RColorBrewer::display.brewer.pal(n=8, name='Set1')

• Color Palette Cheatsheet
• Science journals inspired palettes

Panels - matrices of plots

• facet_wrap
• facet_grid

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))+ 
    facet_wrap(~Plant)

ggplot(CO2) + geom_line(aes(x = conc, y = uptake, colour = Type))+ 
    facet_wrap(~Plant, scales = 'free_y')

ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))+ 
    facet_grid(Type~Treatment)

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
grid.arrange(g1,  g2)

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
g1/g2

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
g1+g2

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_classic()

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_bw()

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_grey()

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_minimal()

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_linedraw()

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_light()

png('resources/xkcd.png', width=500, height=500, res=200)
library(xkcd)  
library(sysfonts)
library(extrafont)
download.file("http://simonsoftware.se/other/xkcd.ttf", dest="xkcd.ttf")
font_import(".")
loadfonts()
xrange <- range(CO2$conc)
yrange <- range(CO2$uptake)
ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth(position='jitter', size=1.5) +
    #geom_point() +
        theme_minimal()+theme(text=element_text(size=16, family='xkcd'))+
        xkcdaxis(xrange, yrange)

dev.off()        

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
ggsave(filename='figure1.pdf', g1,  width=7,  height=5)

head(state)

##            Illiteracy state.region         x       y
## Alabama           2.1        South  -86.7509 32.5901
## Alaska            1.5         West -127.2500 49.2500
## Arizona           1.8         West -111.6250 34.2192
## Arkansas          1.9        South  -92.2992 34.7336
## California        1.1         West -119.7730 36.5341
## Colorado          0.7         West -105.5130 38.6777

Calculate the mean and 95% confidence interval of Illiteracy per state.region and plot them.

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    summarise(Mean = mean(Illiteracy),
              Lower = ci(Illiteracy)[2],
              Upper = ci(Illiteracy)[3])
state.sum

## # A tibble: 4 x 4
##   state.region   Mean Lower Upper
## * <fct>         <dbl> <dbl> <dbl>
## 1 Northeast      1    0.786 1.21 
## 2 South          1.74 1.44  2.03 
## 3 North Central  0.7  0.610 0.790
## 4 West           1.02 0.655 1.39

ggplot(state.sum, aes(y = Mean, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width=0.1)

ggplot(state.sum, aes(y = Mean, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.1) +
    scale_x_discrete("Region") +
    scale_y_continuous("Illiteracy rate (%)")+
    theme_classic() +
    theme(axis.line.y = element_line(),
          axis.line.x = element_line())

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    mutate(mean_sdl(Illiteracy))
state.sum

## # A tibble: 50 x 6
## # Groups:   state.region [4]
##    Illiteracy state.region       x     y   ymin  ymax
##         <dbl> <fct>          <dbl> <dbl>  <dbl> <dbl>
##  1        2.1 South          -86.8  1.74  0.633 2.84 
##  2        1.5 West          -127.   1.02 -0.194 2.24 
##  3        1.8 West          -112.   1.02 -0.194 2.24 
##  4        1.9 South          -92.3  1.74  0.633 2.84 
##  5        1.1 West          -120.   1.02 -0.194 2.24 
##  6        0.7 West          -106.   1.02 -0.194 2.24 
##  7        1.1 Northeast      -72.4  1     0.443 1.56 
##  8        0.9 South          -75.0  1.74  0.633 2.84 
##  9        1.3 South          -81.7  1.74  0.633 2.84 
## 10        2   South          -83.4  1.74  0.633 2.84 
## 11        1.9 West          -126.   1.02 -0.194 2.24 
## 12        0.6 West          -114.   1.02 -0.194 2.24 
## 13        0.9 North Central  -89.4  0.7   0.417 0.983
## 14        0.7 North Central  -86.1  0.7   0.417 0.983
## 15        0.5 North Central  -93.4  0.7   0.417 0.983
## 16        0.6 North Central  -98.1  0.7   0.417 0.983
## 17        1.6 South          -84.8  1.74  0.633 2.84 
## 18        2.8 South          -92.3  1.74  0.633 2.84 
## 19        0.7 Northeast      -69.0  1     0.443 1.56 
## 20        0.9 South          -76.6  1.74  0.633 2.84 
## 21        1.1 Northeast      -71.6  1     0.443 1.56 
## 22        0.9 North Central  -84.7  0.7   0.417 0.983
## 23        0.6 North Central  -94.6  0.7   0.417 0.983
## 24        2.4 South          -89.8  1.74  0.633 2.84 
## 25        0.8 North Central  -92.5  0.7   0.417 0.983
## 26        0.6 West          -109.   1.02 -0.194 2.24 
## 27        0.6 North Central  -99.6  0.7   0.417 0.983
## 28        0.5 West          -117.   1.02 -0.194 2.24 
## 29        0.7 Northeast      -71.4  1     0.443 1.56 
## 30        1.1 Northeast      -74.2  1     0.443 1.56 
## 31        2.2 West          -106.   1.02 -0.194 2.24 
## 32        1.4 Northeast      -75.1  1     0.443 1.56 
## 33        1.8 South          -78.5  1.74  0.633 2.84 
## 34        0.8 North Central -100.   0.7   0.417 0.983
## 35        0.8 North Central  -82.6  0.7   0.417 0.983
## 36        1.1 South          -97.1  1.74  0.633 2.84 
## 37        0.6 West          -120.   1.02 -0.194 2.24 
## 38        1   Northeast      -77.4  1     0.443 1.56 
## 39        1.3 Northeast      -71.1  1     0.443 1.56 
## 40        2.3 South          -80.5  1.74  0.633 2.84 
## 41        0.5 North Central  -99.7  0.7   0.417 0.983
## 42        1.7 South          -86.5  1.74  0.633 2.84 
## 43        2.2 South          -98.8  1.74  0.633 2.84 
## 44        0.6 West          -111.   1.02 -0.194 2.24 
## 45        0.6 Northeast      -72.5  1     0.443 1.56 
## 46        1.4 South          -78.2  1.74  0.633 2.84 
## 47        0.6 West          -120.   1.02 -0.194 2.24 
## 48        1.4 South          -80.7  1.74  0.633 2.84 
## 49        0.7 North Central  -90.0  0.7   0.417 0.983
## 50        0.6 West          -107.   1.02 -0.194 2.24

ggplot(state.sum, aes(y = y, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.1)

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    mutate(mean_cl_boot(Illiteracy))
state.sum

## # A tibble: 50 x 6
## # Groups:   state.region [4]
##    Illiteracy state.region       x     y  ymin  ymax
##         <dbl> <fct>          <dbl> <dbl> <dbl> <dbl>
##  1        2.1 South          -86.8  1.74 1.46  2    
##  2        1.5 West          -127.   1.02 0.731 1.35 
##  3        1.8 West          -112.   1.02 0.731 1.35 
##  4        1.9 South          -92.3  1.74 1.46  2    
##  5        1.1 West          -120.   1.02 0.731 1.35 
##  6        0.7 West          -106.   1.02 0.731 1.35 
##  7        1.1 Northeast      -72.4  1    0.822 1.18 
##  8        0.9 South          -75.0  1.74 1.46  2    
##  9        1.3 South          -81.7  1.74 1.46  2    
## 10        2   South          -83.4  1.74 1.46  2    
## 11        1.9 West          -126.   1.02 0.731 1.35 
## 12        0.6 West          -114.   1.02 0.731 1.35 
## 13        0.9 North Central  -89.4  0.7  0.625 0.775
## 14        0.7 North Central  -86.1  0.7  0.625 0.775
## 15        0.5 North Central  -93.4  0.7  0.625 0.775
## 16        0.6 North Central  -98.1  0.7  0.625 0.775
## 17        1.6 South          -84.8  1.74 1.46  2    
## 18        2.8 South          -92.3  1.74 1.46  2    
## 19        0.7 Northeast      -69.0  1    0.822 1.18 
## 20        0.9 South          -76.6  1.74 1.46  2    
## 21        1.1 Northeast      -71.6  1    0.822 1.18 
## 22        0.9 North Central  -84.7  0.7  0.625 0.775
## 23        0.6 North Central  -94.6  0.7  0.625 0.775
## 24        2.4 South          -89.8  1.74 1.46  2    
## 25        0.8 North Central  -92.5  0.7  0.625 0.775
## 26        0.6 West          -109.   1.02 0.731 1.35 
## 27        0.6 North Central  -99.6  0.7  0.625 0.775
## 28        0.5 West          -117.   1.02 0.731 1.35 
## 29        0.7 Northeast      -71.4  1    0.822 1.18 
## 30        1.1 Northeast      -74.2  1    0.822 1.18 
## 31        2.2 West          -106.   1.02 0.731 1.35 
## 32        1.4 Northeast      -75.1  1    0.822 1.18 
## 33        1.8 South          -78.5  1.74 1.46  2    
## 34        0.8 North Central -100.   0.7  0.625 0.775
## 35        0.8 North Central  -82.6  0.7  0.625 0.775
## 36        1.1 South          -97.1  1.74 1.46  2    
## 37        0.6 West          -120.   1.02 0.731 1.35 
## 38        1   Northeast      -77.4  1    0.822 1.18 
## 39        1.3 Northeast      -71.1  1    0.822 1.18 
## 40        2.3 South          -80.5  1.74 1.46  2    
## 41        0.5 North Central  -99.7  0.7  0.625 0.775
## 42        1.7 South          -86.5  1.74 1.46  2    
## 43        2.2 South          -98.8  1.74 1.46  2    
## 44        0.6 West          -111.   1.02 0.731 1.35 
## 45        0.6 Northeast      -72.5  1    0.822 1.18 
## 46        1.4 South          -78.2  1.74 1.46  2    
## 47        0.6 West          -120.   1.02 0.731 1.35 
## 48        1.4 South          -80.7  1.74 1.46  2    
## 49        0.7 North Central  -90.0  0.7  0.625 0.775
## 50        0.6 West          -107.   1.02 0.731 1.35

ggplot(state.sum, aes(y = y, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.1)

Overlay illiteracy data onto map of US

library(mapdata)
US <- map_data("worldHires", region = "USA")
ggplot(US) +
    geom_polygon(aes(x = long, y = lat, group = group)) +
    geom_point(data = state, aes(y = y, x = x, size = Illiteracy),
               color = "red")

Overlay illiteracy data onto map of US

library(mapdata) 
US <- map_data("worldHires", region="USA")
ggplot(US) +
    geom_polygon(aes(x = long, y = lat, group = group)) +
    geom_point(data = state,aes(y = y, x = x, size = Illiteracy),
               color = "red")+
    coord_map(xlim = c(-150, -50), ylim = c(20, 60)) +
    theme_minimal()

MACNALLY <- read.csv('../data/macnally.csv',
  header = TRUE, row.names = 1, strip.white = TRUE)
head(MACNALLY)

##                 HABITAT GST EYR
## Reedy Lake        Mixed 3.4 0.0
## Pearcedale  Gipps.Manna 3.4 9.2
## Warneet     Gipps.Manna 8.4 3.8
## Cranbourne  Gipps.Manna 3.0 5.0
## Lysterfield       Mixed 5.6 5.6
## Red Hill          Mixed 8.1 4.1

Calculate the mean and standard error of GST and plot them

Calculate the mean and standard error of GST and plot mean and confidence bars

library(gmodels)
ci(MACNALLY$GST)

##   Estimate   CI lower   CI upper Std. Error 
##   4.878378   4.035292   5.721465   0.415704

MACNALLY.agg = MACNALLY %>% group_by(HABITAT) %>%
  summarize(Mean = mean(GST), Lower = ci(GST)[2], Upper = ci(GST)[3])
ggplot(MACNALLY.agg, aes(y = Mean, x = HABITAT)) +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.1)+
    geom_point() + theme_classic()

You can also use ggplot’s summary

library(tidyverse)
MACNALLY.melt = MACNALLY %>%
  pivot_longer(-HABITAT, names_to = "variable",  values_to = "value")
ggplot(MACNALLY.melt, aes(y = value, x = HABITAT)) +
    stat_summary(fun.y = "mean", geom = "point")+
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar",
                 width = 0.1)+
    facet_grid(~variable)

You can also use ggplot’s summary

#and bootstrapped means..
ggplot(MACNALLY.melt, aes(y = value, x = HABITAT)) +
    stat_summary(fun.y = "mean", geom = "point")+
    stat_summary(fun.data = "mean_cl_boot", geom = "errorbar",
                 width = 0.1)+
    facet_grid(~variable)